I built Aether because I lived through how broken Healthcare systems are, across the world. When my mother was sick with cancer (which went undiagnosed right until the day before she passed), I noticed how medical professionals really worked in silos. Neurologists only dealt with reports that mattered to them, haemotologists with those that were related to blood only, and similarly others. Not once did someone decide to look at her entire longitudinal health, piecing together, what was happening with her. I did that on my own, charting her reports on excel, trying to find patterns/coorelations and anomalies. I managed to find a few conditions (not related with her cancer). Doctors in the US, India, Switzerland, Germany, Portugal - all only saw what mattered to them, and not what mattered to the patient. Eventually, her cancer was detected because one smart professional decided to dive into her blood work from the very first blood draw, 3 months later, and found the cancer there. Had there been a longitudinal view, maybe her cancer would have been detected earlier, and treated faster, instead of letting it spread. Maybe I would have had more time with her.
Aether is my ode to her. Aether is my attempt to bring back the patient and her health, back to focus. With Aether, I hope to get to a stage, where nothing gets missed when a medical professional examines a patient.
One idea that deeply shaped Aether comes from rare disease research.
A recent npj Digital Medicine paper shows that rare diseases are underdiagnosed not because data is missing, but because medical context does not compound over time. Diagnoses are provisional. Labels are noisy. Learning has to be longitudinal.
This resonated personally. My mother was diagnosed with an extremely rare cancer only days before her death. Her records existed, but no one ever had a complete picture.
I wrote a longer reflection on what rare disease AI teaches us about longitudinal health here:
https://myaether.live/blog/rare-disease-ai-longitudinal-health
Appreciate the PH community engaging with this perspective.
One of the ideas shaping Aether comes from longitudinal symptom research in oncology.
A recent JCO Clinical Cancer Informatics study shows that future cancer symptom severity can be predicted using sparse, irregular EHR nursing documentation, as long as symptom history is preserved over time.
The key insight is not the model. It is that learning only works when health systems stop throwing away longitudinal context.
I wrote a short reflection on what this teaches us about health graphs and EHR Lite design here:
https://myaether.live/blog/predicting-cancer-symptom-trajectories-longitudinal-ehr
Appreciate the PH community engaging with this perspective.
We’ve been thinking a lot about representation in healthcare AI.
A recent npj Digital Medicine paper built a multimodal sepsis embedding model that outperformed baseline models and even physicians in mortality prediction.
The deeper insight is not about sepsis. It’s about representation learning.
If admission-level embeddings can unlock this much signal, what happens when AI has longitudinal context across years?
We wrote a breakdown here:
https://myaether.live/blog/sepsis-ai-representation-longitudinal-future
Would love thoughts from the PH community.